results {scDD} | R Documentation

## results

### Description

extract results objects after running scDD analysis

### Usage

    results(SCdat, type = c("Genes", "Zhat.c1", "Zhat.c2", "Zhat.combined"))

### Arguments

`SCdat` | An object of class `SingleCellExperiment` that contains normalized single-cell expression and metadata, and the output of the `scDD` function.
`type` | A character variable specifying which output is desired, with possible values "Genes", "Zhat.c1", "Zhat.c2", and "Zhat.overall". The default value is "Genes", which contains a a data frame with nine columns: gene name (matches rownames of SCdat), permutation p-value for testing of independence of condition membership with clustering, Benjamini-Hochberg adjusted version of the previous column, p-value for test of difference in dropout rate (only for non-DD genes), Benjamini-Hochberg adjusted version of the previous column, name of the DD (DE, DP, DM, DB) pattern or DZ (otherwise NS = not significant), the number of clusters identified overall, the number of clusters identified in condition 1 alone, and the number of clusters identified in condition 2 alone. If `type` is "Zhat.c1", then a `matrix` is returned that contains the fitted cluster memberships (partition estimates Z) for each sample (cluster number given by 1,2,3,...) in columns and gene in rows only for condition 1. The same information is returned only for condition 2, and for the overall clustering, when `type` is set to "Zhat.c2" or "Zhat.overall", respectively. Zeroes, which are not involved in the clustering, are labeled as zero.

### Details

Convenient helper function to extract the results (gene classifications, pvalues, and clustering information). Results data.frames/matrices are stored in the `metadata` slot and can also be accessed without the help of this convenience function by calling `metadata(SCdat)`.

### Value

A `data.frame` which contains either the gene classification and p-value results, or cluster membership information, as detailed in the description of the `type` input parameter.

### Examples

    # load toy simulated example SingleCellExperiment object to find DD genes
    data(scDatExSim)

    # set arguments to pass to scDD function

    prior_param=list(alpha=0.01, mu0=0, s0=0.01, a0=0.01, b0=0.01)

    # call the scDD function to perform permutations and classify DD genes

    scDatExSim <- scDD(scDatExSim, prior_param=prior_param, testZeroes=FALSE)

    # extract main results object

    RES <- results(scDatExSim)

[Package scDD version 1.8.0 Index]